5.1.3 Detecting areas of change

Change detection is one of the most common uses of remote sensing, and many methods have been used, tested and proposed in the literature, although there is little information about which methods work best in which situations. In general, at least two dates of images (end-points) are necessary to map change. Image classification methods are commonly used, in which case multiple images are used to make the assignment to stable classes (places that have not changed) as well as change classes, such as Forest to Grassland (Woodcock et al., 2001). Methods use the change in a spectral band, bands or indices as the basis of the change detection process (Lambin and Strahler, 1994). The GOFC-GOLD Sourcebook (GOFC-GOLD, 2015) includes descriptions and examples of several change detection methods(1) and is a useful resource when considering options for combinations of methods and remote sensing data to be used for mapping change.

Methods that use many images, or a time-series of images, have been developed and tested (Chen et al., 2004; Kennedy et al., 2007; Furby et al., 2008; Zhuravleva et al., 2013). These approaches have many advantages, as they are not so dependent on the conditions at the time the individual images were collected. Use of a time-series of images can help avoid some kinds of errors in the monitoring of forest change. Box 23 provides more detail on time series analysis.

Detecting change in land cover is not sufficient to map change in land use, which is needed for consistency with IPCC guidance. To make the distinction between deforested areas and areas where crown cover has been removed but forest land use remains (as needed for Row 2 in Table 13)(2), georeferenced areas of forest planted annually or allowed to regenerate naturally within managed forest should be obtained from national forest authorities and stakeholders via the NFMS. The existence of planted or regenerating forest on these areas confirmed as the appearance of the corresponding pixels merges with the appearance of other pixels with this forest type. This also applies to areas which may have the appearance of deforestation but which have in fact been subject to natural disturbance such as wildfires, cyclones, or pest outbreaks. Use of local information such as forest type and management intent, climatic extremes such as drought, and records of natural disturbance will be useful in aiding the translation of imagery into reliable activity data. Time series data (Box 23) can also help make the distinction.

The methods described in Chapter 3, Section 3.1.2for estimating GHG emissions and removals associated with degradation require stratification (or categorization) of forests into primary, modified natural forest, and planted, or another stratification used by a country.There may be sub-stratification to capture different forest ecosystems or types of human intervention. This can be achieved by a combination of remotely-sensed data (in most cases 30m resolution or finer) and supplementary data (such as concession boundaries, land use planning data and information on infrastructure).

Stratification plus reference data provides the activity data. NFI or equivalent field sampling possibly in combination with LIDAR or SAR data are used to identify carbon densities and long-term trends that provide the emission/removal factors needed. Stratification using remote sensing can also be used in the design of sampling strategies for the stock change approach. Methods to use remote sensing to map areas undergoing degradation or other change include use spectral indices (combinations of spectral bands designed to accentuate surface characteristics), spectral mixture analysis, and textural analysis. Visual methods can also be effective for stratifying forests on the basis of degradation. Examples of identifying degraded forest areas can be found in Winrock International, 2012; Souza et al., 2013; and Bryan et al., 2013.

Mitchell (2014) has provided a summary of evolving requirements and capabilities in forest degradation monitoring, based on the on the expert workshop on approaches to monitoring forest degradation organized jointly by GFOI, GOFC-GOLD and the European Space Agency in October 2013. Table 14, based on table 5 in the workshop report, summarizes the use of different remotely sensed data sources in combination with ground-based and supplementary data to monitor processes that can lead to long-term decline in carbon stocks, either by transfer of forest areas from carbon strata from those with higher to lower carbon density, or (if the intensity of the activity increases) to decline of long-term carbon density within a given stratum. The relationship of these possibilities to degradation is discussed in Chapter 3, Section 3.1.2.

Table 14: Useful data sources for use in combination to estimate emissions from degradation

Process (d)

Activity Data

Emission/removal factors

Auxiliary Data

Tree removal – clear cut

Optical: 30m or

better (a); SAR L (b) or C band (c)

Sample based:

Repeated NFI or permanent plots (a)

Field sampling: disturbed

vs. undisturbed areas (a)

Terrestrial LIDAR (b)

RS combined with field

observations (NFI or

bespoke):

Height changes: airborne LIDAR (b),

InSAR height differences (c)

Backscatter derived AGB

estimates (c)

VHR Optical texture based

AGB model (c)

Forest concession boundaries

Land use plans

Harvest estimates combined with growth estimates

Settlements transport network (road, rivers)

Data from local communities

and stakeholders (f)

Tree removal – selective with infrastructure

Optical: 30m or

better (a); SAR L (c), C (c) or X (c) band

Tree removal – selective without infrastructure

Optical: 5m or better

(b); SAR X (c) band

Forest area affected by shifting cultivation

Optical: 30m or

better (a); SAR L band (b) or C band (c)

Settlements transport network (roads, rivers)

Forest concession boundaries

Fire – fire scars

Optical: 30m or

better (a); SAR L band (b) or C band

(c)

Of possible relevance to assessing fire frequency or fuel load:

Forest concessions boundaries

Land use plans

Settlements transport network (road, rivers)

Fires – ground fire

Optical: 5m or better (b); SAR X band (c)

Forest area affected by agroforestry

Optical: 5m or better

(c)

Sample based:

Repeated NFI or permanent plots (a)

Field sampling: disturbed vs. undisturbed areas (a)

Terrestrial LIDAR (b)

Land use plans

Settlements transport network (road, rivers)

Fuel wood and charcoal extraction

Mostly not detectable from RS

Population consumption per capita

Forest growth potential

GIS models

Forest grazing

Livestock estimates and consumption rates

Forest growth potential

GIS models

Forest regrowth following clearcuts, logging roads, fire scars (e)

Optical: 30m or

better (b)

SAR L band (b) or X

band (c)

Sample based:

Repeated NFI or permanent plots (a)

Previous disturbance from activity data

Reference: (Mitchell 2014, with modification)

Notes: a) The method is operational in countries; b) Large-scale demonstrations of the method exist; c) The methods have been demonstrated; d) These processes can lead to transfer of forest areas from strata with higher to lower carbon density, or (if intensified over time) to reduced carbon densities within given strata, which can be associated with degradation as described in Chapter 3, Section 3.1.2; e) Relevant to long-run carbon densities; f) Not repeated in every row, but of possible relevance in most cases.

Box 23: Time series analysis of earth observations for monitoring of activity data

A time series is a sequence of observations taken sequentially in time. Adjacent observations are typically dependent and time series analysis is concerned with techniques for analysis of this dependency (Box et al., 1994). In the context of activity data, each point in the series is interpreted in the same way as a single image (e.g. by visual interpretation or semi-automated algorithms) with the advantage that additional information can be obtained by considering the series as a whole.

It is useful to distinguish between two or a few images over a study period (e.g. 10-15 years) and an annual or higher frequency of observations. It is easy to imagine that having many observations of the land surface rather than just two snapshots in time allows for a more comprehensive analysis of surface activities. Yet, traditional image analyses of land cover and land change have often relied on few images because of the cost of acquiring suitable imagery. The opening of the Landsat archive in 2008 (Woodcock et al., 2008) relaxed this constraint, and a time series of Landsat observations (with an 8-16 day revisit time) can be obtained for virtually any place on Earth. Other data sources are available but the combination of a free, open and extensive archive with the temporal and spatial characteristics of Landsat data makes it a highly useful for time series analysis.

Time series analysis allows tracking activities rather than creating a map that represents conditions at one point in time or a change map between two points. It enables characterization of post-disturbance landscapes and gradual and continuous activities such as regrowing forest and forest degradation. In the following example (adapted from Kennedy et al., 2014), a forest was cleared and then allowed to regenerate. In the figure below only 2 observations in time are available in (a), 5 in (b), whereas a dense time series is available in (c) that allows for an accurate representation of the activity.

With just two observations (a) it appears as if the land surface variable (which could be a surface reflectance, backscatter or a vegetation index) being observed is showing a slight decrease. The situation is improved with several observations available (b) that provide some evidence of the disturbance event and the subsequent recovery. Still, the land surface activities are not readily identified nor are the timing of events. With many observations (c) the analyst can determine the timing and magnitude of the logging event and characterize the recovery in time and space. Provided that the carbon content of the forest that was logged and carbon dynamics of the recovering forest are known, the analyst could estimate the amount of carbon emitted from both the soil and decomposing logged wood, and the amount carbon sequestered in the recovering forest and soil following the logging event. Examples of operational systems that use this method include Canada, Australia and Indonesia (see Appendix C), all of which use the integration tools detailed in Chapter 3, Section 3.2.

To achieve the results illustrated the figure above, it is possible to create pixel-level composites by applying a statistic (median or max value for example) to a fixed number of observations, select the best images according to some criterion (growing season, minimum cloud cover, etc.), or try to use all of available observations. Composites and “best images” approaches have the advantage of reducing the amount of data to be analysed but information on land activities is reduced compared to an “all observations” approach. The latter enables a detailed analysis of the landscape but requires considerable storage and computing capabilities.

Composite-based approaches have proved successful for large scale change mapping and been used for making global maps of tree cover change at annual basis (Hansen et al., 2013). The same is true for “best images” approaches, which have been used for creating global change maps at five year interval (Kim et al., 2014). The latter has the advantage of reduction in data volume which allows algorithms to faster process the data which in turn enables the analyst to revisit the training data and redo and refine the classification process more often. Several composite-based algorithms for change detection have been published since the opening of the Landsat archive (e.g. Griffiths et al., 2014; Huang et al., 2010; Kennedy et al., 2010) and cloud computing platforms such as Google Earth Engine can be used to create composites for large areas without downloading the data. If having direct access to the satellite data, the BEEODA virtual machine contains open source algorithms for compositing.

While composite-based methods are powerful, the reduction of data also implies that there are observations of the area of interest that are not being used. Algorithms such as CCDC (Holden, 2015; Zhu et al., 2012; Zhu & Woodcock, 2014b) and BFAST (Verbesselt et al., 2010; Verbesselt et al., 2012; DeVries et al., 2015) are examples of change detection algorithms that analyses all available observations. The approach is more computationally intensive and requires detailed screening for clouds and cloud shadows but it enables a more comprehensive analysis of the land surface. It allows for studies of phenology and seasonality, and for a more detailed analysis of post-disturbance landscapes, especially dynamic landscapes that exhibit rapid change.

Use of archives other than Landsat will grow in future as the archives of other satellite missions develop, and as new free data missions are launched. For example, the Sentinel-2 mission will generate data that when combined with Landsat data will enhance time series analysis of the land surface. SAR data, which can provide more stationary time series because of cloud penetrating capabilities, are also likely to enhance the analysis when combined with optical data (Reiche et al., 2015). Time series analysis of radar alone is now facilitated with the advent of Sentinel-1 data that is available free of charge. Although both CCDC and BFAST (Verbesselt et al., 2012; Xin et al., 2013) have been used with coarse resolution data (MODIS) for near real time monitoring of forest disturbance, these data are not usually used for mapping activity data because of their coarse spatial resolution.

Time series make reference data collection somewhat more complex and time consuming, which may result in a smaller sample size, but with tools such as TimeSync (Cohen et al., 2010), BFAST Spatial and TSTools the collection of temporal reference observations is possible. A suitable approach is to collect annual reference observations enabling computation of annual unbiased estimates and confidence intervals (Cohen et al., 2016).

Such data are required as the existence of planted or regenerating forest on these areas requires confirmation as the appearance of the corresponding pixels merges with the appearance of other pixels with this forest type.